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Record W1582387859

COVERAGE PROBLEM IN HETEROGENEOUS WIRELESS SENSOR NETWORKS

2013· article· en· W1582387859 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEuropean Scientific Journal ESJ · 2013
Typearticle
Languageen
FieldComputer Science
TopicEnergy Efficient Wireless Sensor Networks
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsComputer networkRelayWireless sensor networkComputer scienceKey distribution in wireless sensor networksNode (physics)Energy consumptionRouting (electronic design automation)Process (computing)Path (computing)WirelessWireless networkPower (physics)TelecommunicationsEngineering
DOInot available

Abstract

fetched live from OpenAlex

A heterogeneous wireless sensor network consists of different types of nodes in sequence. Some of these nodes have high process powers and significant energy, which are called the manager nodes or super-nodes. The second type nodes, which have normal process power, are only used as monitoring nodes or act as relay nodes in the path to the manager nodes are called the normal nodes. In this paper, an energy-aware algorithm is presented for the optimum selection of sensor and relay groups that are used for monitoring and sending messages from goals in point coverage, using the competition between the nodes. This algorithm is effective in decreasing the energy consumption of the network and increasing its life-time. Moreover, providing that no node saves the information about the routing table and relay nodes; therefore, it will have less complexity and overload.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.354
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0040.001
Open science0.0020.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.009
GPT teacher head0.198
Teacher spread0.189 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it